Nothing
.calc_extBIC_MLE <- function(trait=NULL, currentX=NULL, MMt=NULL, geno=NULL, Zmat=NULL, numberSNPselected=0, quiet=TRUE, lambda=NULL, eig.L=NULL)
{
## internal function: used by AM
## smallest extBIC and BIC is best
## internal function: use in AM only
res_p <- emma.MLE(y=trait, X= currentX , K=MMt, Z=Zmat, llim=-100,ulim=100,
eig.L=eig.L)
BIC <- -2 * res_p$ML + (ncol(currentX)+1) * log(length(trait)) ## fixed effects + variance component
# calculate lambda
if(is.null(lambda)){
lambda <- 1
# found this to be anti-conservative when sample size is small
#lambda <- log(geno$dim_of_M[2])/log(length(trait))
#lambda <- 1-(1/(2*lambda))
} ## outer if
if(length(lambda)==1){
extBIC <- BIC + 2 * lambda *lchoose(geno$dim_of_M[2], numberSNPselected)
if(!quiet){
cat(" Lambda = ", lambda, "\n")
}
} else {
extBIC <- rep(NA, length(lambda))
for(ii in 1:length(lambda)){
extBIC[ii] <- BIC + 2 * lambda[ii] *lchoose(geno$dim_of_M[2], numberSNPselected)
}
}
return(extBIC)
}
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